ArticleJournal of chemical information and modeling2013
FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach.
Article in Journal of chemical information and modeling, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Targeting WEE1 kinase: an integrated machine learning-cheminformatics framework for ultra-large-scale virtual screening and novel inhibitor discovery.Journal of computer-aided molecular design · 2026Article
- Utility of the Morgan Fingerprint in Structure-Based Virtual Ligand Screening.The journal of physical chemistry. B · 2024Article
- FRAGSITE2: A structure and fragment-based approach for virtual ligand screening.Protein science : a publication of the Protein Society · 2024Article
- Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein-Protein Interactions.The journal of physical chemistry. B · 2022Article
- De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference.International journal of molecular sciences · 2022Article
- Ultrahigh Throughput Protein-Ligand Docking with Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Molecular docking-based computational platform for high-throughput virtual screening.CCF transactions on high performance computing · 2022Article
- FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening.Journal of chemical information and modeling · 2021Article
- Identification of Potential HCV Inhibitors Based on the Interaction of Epigallocatechin-3-Gallate with Viral Envelope Proteins.Molecules (Basel, Switzerland) · 2021Article
- Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets.Stroke and vascular neurology · 2020Review
- ClusPro LigTBM: Automated Template-based Small Molecule Docking.Journal of molecular biology · 2020Article
- MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.Molecular pharmaceutics · 2020Article
- Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.Journal of computer-aided molecular design · 2019Article
- Chemical space of Escherichia coli dihydrofolate reductase inhibitors: New approaches for discovering novel drugs for old bugs.Medicinal research reviews · 2019Review
- FINDSITEJournal of chemical information and modeling · 2018Article
- Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates.Structure (London, England : 1993) · 2018Article
- Repurposed FDA-approved drugs targeting genes influencing aging can extend lifespan and healthspan in rotifers.Biogerontology · 2018Article
- Pocket detection and interaction-weighted ligand-similarity search yields novel high-affinity binders for Myocilin-OLF, a protein implicated in glaucoma.Bioorganic & medicinal chemistry letters · 2017Article
- Why Is There a Glass Ceiling for Threading Based Protein Structure Prediction Methods?The journal of physical chemistry. B · 2017Article
- Repurposing FDA-approved drugs for anti-aging therapies.Biogerontology · 2016Article
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2 authors.
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Abstract
Virtual ligand screening is an integral part of the modern drug discovery process. Traditional ligand-based, virtual screening approaches are fast but require a set of structurally diverse ligands known to bind to the target. Traditional structure-based approaches require high-resolution target protein structures and are computationally demanding. In contrast, the recently developed threading/structure-based FINDSITE-based approaches have the advantage that they are as fast as traditional ligand-based approaches and yet overcome the limitations of traditional ligand- or structure-based approaches. These new methods can use predicted low-resolution structures and infer the likelihood of a ligand binding to a target by utilizing ligand information excised from the target's remote or close homologous proteins and/or libraries of ligand binding databases. Here, we develop an improved version of FINDSITE, FINDSITE(filt), that filters out false positive ligands in threading identified templates by a better binding site detection procedure that includes information about the binding site amino acid similarity. We then combine FINDSITE(filt) with FINDSITE(X) that uses publicly available binding databases ChEMBL and DrugBank for virtual ligand screening. The combined approach, FINDSITE(comb), is compared to two traditional docking methods, AUTODOCK Vina and DOCK 6, on the DUD benchmark set. It is shown to be significantly better in terms of enrichment factor, dependence on target structure quality, and speed. FINDSITE(comb) is then tested for virtual ligand screening on a large set of 3576 generic targets from the DrugBank database as well as a set of 168 Human GPCRs. Excluding close homologues, FINDSITE(comb) gives an average enrichment factor of 52.1 for generic targets and 22.3 for GPCRs within the top 1% of the screened compound library. Around 65% of the targets have better than random enrichment factors. The performance is insensitive to target structure quality, as long as it has a TM-score ≥ 0.4 to native. Thus, FINDSITE(comb) makes the screening of millions of compounds across entire proteomes feasible. The FINDSITE(comb) web service is freely available for academic users at http://cssb.biology.gatech.edu/skolnick/webservice/FINDSITE-COMB/index.html.
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